Huifang Chen

Arctic sea ice can be regarded as a sensitive indicator of climate change, and it has declined dramatically in recent decades. The swift decline in Arctic sea ice coverage leads to an expansion of the marginal ice zone (MIZ). In this study, an ice-based buoy with an imaging system is designed for the long-term observation of the changes in sea ice from the packed ice zone to the marginal ice zone in polar regions. The system composition, main buoy, image system, and buoy load were analyzed. An underwater camera supports a 640 × 480 resolution image acquisition, RS485 communication, stable operation at –40 °C, and long-term underwater sealing protection through a titanium alloy housing. During a continuous three-month field deployment in the Arctic, the system successfully captured images of ice-bottom morphology and biological attachment, demonstrating imaging reliability and operational stability under extreme conditions. In addition, the buoy employed a battery state estimation method based on the Extreme Learning Machine (ELM). Compared with LSTM, BP, BiLSTM, SAELSTM, and RF models, the ELM achieved a test set performance of RMSE = 0.05 and MAE = 0.187, significantly outperforming the alternatives and thereby improving energy management and the reliability of long-term autonomous operation. Laboratory flume tests further verified the power generation performance of the wave energy-assisted supply system. However, due to the limited duration of Arctic deployment, full year-round performance has not yet been validated, and the imaging resolution remains insufficient for biological classification. The results indicate that the buoy demonstrates strong innovation and application potential for long-term polar observations, while further improvements are needed through extended deployments and enhanced imaging capability.

In this article, the cooperative formation control problem of an air‐sea heterogeneous unmanned system (ASHS) under adaptive switching topology is investigated. And a cooperative control method, consisting of a cooperative path‐planning algorithm for leaders in different unmanned domains and a formation control algorithm for followers in each unmanned domain, is proposed. The proposed method involves four aspects. First, a neighboring node decision mechanism is designed to determine the neighboring nodes of each unmanned vehicle and establish the network topology of the ASHS, where both broadcast and unicast communication modes are considered. Second, to reduce communication energy consumption for the cooperative control, a greedy optimization‐based topology switching mechanism is presented to minimize the number of neighboring nodes of each unmanned vehicle. Third, a cooperative path‐planning algorithm using an improved artificial potential field is proposed to handle the path planning with obstacle avoidance for leaders in different unmanned domains. Finally, assuming that the obstacle avoidance by followers is superior to the formation control by the group, an adaptive hierarchical formation control algorithm is proposed for followers in each unmanned domain. Simulation results show that the cooperative control of the ASHS can be achieved with the proposed method. Moreover, from the perspective of communication energy consumption for the cooperative formation control in the ASHS, the broadcast mode is a good choice for the state information exchange.

Multipath TCP (MPTCP) can aggregate the bandwidth of all connected paths, providing high data rate and transmission reliable service in wireless networks, especially for video streaming. To avoid network congestion and be fair to TCP users, extending the single-path congestion control to multipath has become a hot research issue in MPTCP. Because coupled multipath congestion control dominates existing MPTCP strategies, we analyze four classic coupled congestion control algorithms from the perspective of bandwidth competition, indicating that they can only achieve trade-off between the aggregate throughput and fairness on the shared bottleneck. Some shared bottleneck detection based MPTCP congestion control strategies fully utilize the available bandwidth on non-bottleneck links, but being unfair to legacy TCP compared to the coupled algorithms. This motives us to propose a new Shared bottleneck Detection based Decoupled Congestion Control (SD-DCC) scheme, which includes a more applicable shared bottleneck detection method and more suitable decoupled congestion control strategy in consideration of the wireless link conditions. The performance of our proposed SD-DCC scheme is evaluated through extensive simulations, which demonstrates that the SD-DCC scheme achieves better transmission performance to meet the requirements of video streaming services, while keeping fairness to legacy TCP users.

In the framework of the space-air-ground-ocean integrated network, the underwater acoustic sensor network (UASN) plays a pivotal role. The design of media access control (MAC) protocols is essential for the UASN to ensure efficient and reliable data transmission. From the perspective of differentiated services in the UASN, a service-aware and scheduling-based hybrid MAC protocol, named the SSH-MAC protocol, is proposed in this paper. In the SSH-MAC protocol, the centralized scheduling strategy is adopted by sensor nodes with environmental monitoring service, and the distributed scheduling strategy is adopted by sensor nodes with target detection service. Considering the time-varying data generation rate of sensor nodes, the sink node will switch the scheduling mode of sensor nodes based on the specific control packet and adjust the resource allocation ratio between centralized scheduling and distributed scheduling. Simulation results show that the performance of the SSH-MAC protocol, in terms of utilization, end-to-end delay, packet delivery ratio, energy consumption, and payload efficiency, is good.

The underwater acoustic sensor network (UASN) is crucial for monitoring marine environments and detecting underwater targets. Reliable networking, especially routing technique, is essential in the UASN due to the challenges of underwater acoustics. In this paper, an intelligent routing method based on graph neural network, named as the GNN-IR method, is proposed to address complex network topology and adapt to dynamic network environment. The proposed GNN-IR method leverages the GNN’s ability to process non-Euclidean data and its strong predictive capability. It integrates the UASN’s characteristics by building a model of network state prediction and designing the corresponding functions. Additionally, a prediction validity period mechanism in the GNN-IR method is designed to balance packet delivery ratio (PDR) and network energy efficiency. Simulations show that the GNN-IR method performs well in terms of effective data transmission, long network lifetime, and low additional routing information overhead. Furthermore, the proposed routing method is demonstrated an enhanced adaptability and flexibility in the complex underwater environment.

Due to the relative motion between transmitters and receivers and the multipath characteristic of wideband underwater acoustic channels, Doppler and channel estimations are of great significance for an underwater acoustic (UWA) communication system. In this paper, a preamble signal based on superimposed linear frequency modulation (LFM) signals is first designed. Based on the designed preamble signal, a real-time Doppler factor estimation algorithm is proposed. The relative correlation peak shift of two LFM signals in the designed preamble signal is utilized to estimate the Doppler factor. Moreover, an enhanced channel estimation algorithm, the correlation-peak-search-based improved orthogonal matching pursuit (CPS-IOMP) algorithm, is also proposed. In the CPS-IOMP algorithm, the excellent autocorrelation characteristic of the designed preamble signal is used to estimate the channel sparsity and multipath delays, which are utilized to construct the simplified dictionary matrix. The simulation and sea trial data analysis results validated the designed preamble, the proposed Doppler estimation algorithm, and the channel estimation algorithm. The performance of the proposed Doppler factor estimation is better than that of the block estimation algorithm. Compared with the original OMP algorithm with known channel sparsity, the proposed CPS-IOMP algorithm achieves a similar estimation accuracy with a smaller computational complexity, as well as requiring no prior knowledge about the channel sparsity.

The underwater acoustic sensor network (UASN) plays a crucial role in collecting real-time data from remote areas of the ocean. However, the deployment of UASN poses significant challenges due to the demanding environmental conditions and the considerable expenses associated with its implementation. Therefore, it is essential to design an appropriate routing protocol to effectively address the issues of packet delivery delay, routing void, and energy consumption. In this paper, an adaptive support vector machine (SVM)-based routing (ASVMR) protocol is proposed for the UASN to minimize end-to-end delay and prolong the network lifetime. The proposed protocol employs a distributed routing approach that dynamically optimizes the routing path in real time by considering four types of node state information. Moreover, the ASVMR protocol establishes a “routing vector” spanning from the current node to the sink node and selects a suitable pipe radius according to the packet delivery ratio (PDR). In addition, the ASVMR protocol incorporates future states of sensor nodes into the decision-making process, along with the adoption of a waiting time mechanism and routing void recovery mechanism. Extensive simulation results demonstrate that the proposed ASVMR protocol performs well in terms of the PDR, the hop count, the end-to-end delay, and the energy efficiency in dynamic underwater environments.